docs: def predict(
              self,
              peptides,
              alleles=None,
              allele=None,
              throw=True,
              centrality_measure=DEFAULT_CENTRALITY_MEASURE):
          """
          Predict nM binding affinities.
          
          If multiple predictors are available for an allele, the predictions are
          the geometric means of the individual model predictions.
          
          One of 'allele' or 'alleles' must be specified. If 'allele' is specified
          all predictions will be for the given allele. If 'alleles' is specified
          it must be the same length as 'peptides' and give the allele
          corresponding to each peptide.
          
          Parameters
          ----------
          peptides : `EncodableSequences` or list of string
          alleles : list of string
          allele : string
          throw : boolean
              If True, a ValueError will be raised in the case of unsupported
              alleles or peptide lengths. If False, a warning will be logged and
              the predictions for the unsupported alleles or peptides will be NaN.
          centrality_measure : string or callable
              Measure of central tendency to use to combine predictions in the
              ensemble. Options include: mean, median, robust_mean.
  
          Returns
          -------
          numpy.array of predictions
          """
          df = self.predict_to_dataframe(
              peptides=peptides,
              alleles=alleles,
              allele=allele,
              throw=throw,
              include_percentile_ranks=False,
              include_confidence_intervals=False,
              centrality_measure=centrality_measure,
          )
          return df.prediction.values